[Paddle Quantum (量桨)](https://qml.baidu.com/) is a quantum machine learning (QML) toolkit developed based on Baidu PaddlePaddle. It provides a platform to construct and train quantum neural networks (QNNs) with easy-to-use QML development kits suporting combinatorial optimization, quantum chemistry and other cutting-edge quantum applications, making PaddlePaddle the first and only deep learning framework in China that supports quantum machine learning.
[Paddle Quantum (量桨)](https://qml.baidu.com/) is a quantum machine learning (QML) toolkit developed based on Baidu PaddlePaddle. It provides a platform to construct and train quantum neural networks (QNNs) with easy-to-use QML development kits supporting combinatorial optimization, quantum chemistry and other cutting-edge quantum applications, making PaddlePaddle the first and only deep learning framework in China that supports quantum machine learning.
@@ -55,7 +55,7 @@ Paddle Quantum aims at establishing a bridge between artificial intelligence (AI
## Features
- Easy-to-use
- Many online learning resources (17+ tutorials)
- Many online learning resources (23+ tutorials)
- High efficiency in building QNN with various QNN templates
- Automatic differentiation
- Versatile
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@@ -66,7 +66,6 @@ Paddle Quantum aims at establishing a bridge between artificial intelligence (AI
- Toolboxes for Chemistry & Optimization
- LOCCNet for distributed quantum information processing
- Self-developed QML algorithms
## Install
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@@ -110,7 +109,7 @@ cd paddle_quantum/QAOA/example
python main.py
```
> For the introduction of QAOA, please refer to our [QAOA tutorial](https://github.com/PaddlePaddle/Quantum/tree/master/tutorial/QAOA).
> For the introduction of QAOA, please refer to our [QAOA tutorial](https://github.com/PaddlePaddle/Quantum/tree/master/tutorial/combinatorial_optimization/QAOA_EN.ipynb).
## Introduction and developments
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@@ -126,27 +125,42 @@ python main.py
### Tutorials
We provide tutorials covering combinatorial optimization, quantum chemistry, quantum classification, quantum entanglement manipulation and other popular QML research topics. Each tutorial currently supports reading on our website and running Jupyter Notebooks locally. For interested developers, we recommend them to download Jupyter Notebooks and play around with it. Here is the tutorial list,
8.[Variational Quantum State Diagonalization (VQSD)](./tutorial/VQSD)
9.[Gibbs State Preparation](./tutorial/Gibbs)
10.[Variational Quantum Singular Value Decomposition (VQSVD)](./tutorial/VQSVD)
11.[Local Operations and Classical Communication in QNN (LOCCNet)](./tutorial/LOCC/LOCCNET_Tutorial_EN.ipynb)
12.[Entanglement Distillation -- the BBPSSW protocol](./tutorial/LOCC)
13.[Entanglement Distillation -- the DEJMPS protocol](./tutorial/LOCC)
14.[Entanglement Distillation -- Protocol design with LOCCNet](./tutorial/LOCC)
15.[Quantum Teleportation](./tutorial/LOCC)
16.[Quantum State Discrimination](./tutorial/LOCC)
17.[Noise Model and Quantum Channel](./tutorial/Noise)
With the latest LOCCNet module, Paddle Quantum can efficiently simulate distributed quantum information processing tasks. Interested readers can start with this [tutorial on LOCCNet](./tutorial/LOCC/LOCCNET_Tutorial_EN.ipynb). In addition, Paddle Quantum supports QNN training on GPU. For users who want to get into more details, please check out the tutorial [Use Paddle Quantum on GPU](./introduction/PaddleQuantum_GPU_EN.ipynb). Moreover, Paddle Quantum could design robust quantum algorithms under noise. For more information, please see [Noise tutorial](./tutorial/Noise/Noise_EN.ipynb).
We provide tutorials covering quantum simulation, machine learning, combinatorial optimization, local operations and classical communication (LOCC), and other popular QML research topics. Each tutorial currently supports reading on our website and running Jupyter Notebooks locally. For interested developers, we recommend them to download Jupyter Notebooks and play around with it. Here is the tutorial list,
6.[Quantum State Discrimination](./tutorial/locc/StateDiscrimination_EN.ipynb)
-[QNN Research](./tutorial/qnn_research)
1.[The Barren Plateaus Phenomenon on Quantum Neural Networks (Barren Plateaus)](./tutorial/qnn_research/BarrenPlateaus_EN.ipynb)
2.[Noise Model and Quantum Channel](./tutorial/qnn_research/Noise_EN.ipynb)
With the latest LOCCNet module, Paddle Quantum can efficiently simulate distributed quantum information processing tasks. Interested readers can start with this [tutorial on LOCCNet](./tutorial/locc/LOCCNET_Tutorial_EN.ipynb). In addition, Paddle Quantum supports QNN training on GPU. For users who want to get into more details, please check out the tutorial [Use Paddle Quantum on GPU](./introduction/PaddleQuantum_GPU_EN.ipynb). Moreover, Paddle Quantum could design robust quantum algorithms under noise. For more information, please see [Noise tutorial](./tutorial/qnn_research/Noise_EN.ipynb).
### API documentation
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@@ -169,17 +183,19 @@ We also highly encourage developers to use Paddle Quantum as a research tool to
So far, we have done several projects with the help of Paddle Quantum as a powerful QML development platform.
[1] Wang, Y., Li, G. & Wang, X. Variational quantum Gibbs state preparation with a truncated Taylor series. arXiv:2005.08797 (2020). [[pdf](https://arxiv.org/pdf/2005.08797.pdf)]
[1] Wang, Youle, Guangxi Li, and Xin Wang. "Variational quantum gibbs state preparation with a truncated taylor series." arXiv preprint arXiv:2005.08797 (2020). [[pdf](https://arxiv.org/pdf/2005.08797.pdf)]
[2] Wang, X., Song, Z. & Wang, Y. Variational Quantum Singular Value Decomposition. arXiv:2006.02336 (2020). [[pdf](https://arxiv.org/pdf/2006.02336.pdf)]
[2] Wang, Xin, Zhixin Song, and Youle Wang. "Variational Quantum Singular Value Decomposition." arXiv preprint arXiv:2006.02336 (2020). [[pdf](https://arxiv.org/pdf/2006.02336.pdf)]
[3] Li, G., Song, Z. & Wang, X. VSQL: Variational Shadow Quantum Learning for Classification. arXiv:2012.08288 (2020). [[pdf]](https://arxiv.org/pdf/2012.08288.pdf), to appear at **AAAI 2021** conference.
[3] Li, Guangxi, Zhixin Song, and Xin Wang. "VSQL: Variational Shadow Quantum Learning for Classification." arXiv preprint arXiv:2012.08288 (2020). [[pdf]](https://arxiv.org/pdf/2012.08288.pdf), to appear at **AAAI 2021** conference.
[4] Chen, R., Song, Z., Zhao, X. & Wang, X. Variational Quantum Algorithms for Trace Distance and Fidelity Estimation. arXiv:2012.05768 (2020). [[pdf]](https://arxiv.org/pdf/2012.05768.pdf)
[4] Chen, Ranyiliu, et al. "Variational Quantum Algorithms for Trace Distance and Fidelity Estimation." arXiv preprint arXiv:2012.05768 (2020). [[pdf]](https://arxiv.org/pdf/2012.05768.pdf)
[5] Wang, K., Song, Z., Zhao, X., Wang Z. & Wang, X. Detecting and quantifying entanglement on near-term quantum devices. arXiv:2012.14311 (2020). [[pdf]](https://arxiv.org/pdf/2012.14311.pdf)
[5] Wang, Kun, et al. "Detecting and quantifying entanglement on near-term quantum devices." arXiv preprint arXiv:2012.14311 (2020). [[pdf]](https://arxiv.org/pdf/2012.14311.pdf)
[6] Zhao, X., Zhao, B., Wang, Z., Song, Z., & Wang, X. LOCCNet: a machine learning framework for distributed quantum information processing. arXiv:2101.12190 (2021). [[pdf]](https://arxiv.org/pdf/2101.12190.pdf)
[6] Zhao, Xuanqiang, et al. "LOCCNet: a machine learning framework for distributed quantum information processing." arXiv preprint arXiv:2101.12190 (2021). [[pdf]](https://arxiv.org/pdf/2101.12190.pdf)
[1] Wang, Y., Li, G. & Wang, X. Variational quantum Gibbs state preparation with a truncated Taylor series. arXiv:2005.08797 (2020). [[pdf](https://arxiv.org/pdf/2005.08797.pdf)]
[2] Wang, X., Song, Z. & Wang, Y. Variational Quantum Singular Value Decomposition. arXiv:2006.02336 (2020). [[pdf](https://arxiv.org/pdf/2006.02336.pdf)]
目前使用 Paddle Quantum 的代表性工作包括了吉布斯态的制备和变分量子奇异值分解：
[3] Li, G., Song, Z. & Wang, X. VSQL: Variational Shadow Quantum Learning for Classification. arXiv:2012.08288 (2020). [[pdf]](https://arxiv.org/pdf/2012.08288.pdf), to appear at **AAAI 2021** conference.
[1] Wang, Youle, Guangxi Li, and Xin Wang. "Variational quantum gibbs state preparation with a truncated taylor series." arXiv preprint arXiv:2005.08797 (2020). [[pdf](https://arxiv.org/pdf/2005.08797.pdf)]
[4] Chen, R., et al. Variational Quantum Algorithms for Trace Distance and Fidelity Estimation. arXiv:2012.05768 (2020). [[pdf]](https://arxiv.org/pdf/2012.05768.pdf)
[2] Wang, Xin, Zhixin Song, and Youle Wang. "Variational Quantum Singular Value Decomposition." arXiv preprint arXiv:2006.02336 (2020). [[pdf](https://arxiv.org/pdf/2006.02336.pdf)]
[5] Wang, K., et al. Detecting and quantifying entanglement on near-term quantum devices. arXiv:2012.14311 (2020). [[pdf]](https://arxiv.org/pdf/2012.14311.pdf)
[3] Li, Guangxi, Zhixin Song, and Xin Wang. "VSQL: Variational Shadow Quantum Learning for Classification." arXiv preprint arXiv:2012.08288 (2020). [[pdf]](https://arxiv.org/pdf/2012.08288.pdf), to appear at **AAAI 2021** conference.
[6] Zhao, X., Zhao, B., Wang, Z., Song, Z., & Wang, X. LOCCNet: a machine learning framework for distributed quantum information processing. arXiv:2101.12190 (2021). [[pdf]](https://arxiv.org/pdf/2101.12190.pdf)
[4] Chen, Ranyiliu, et al. "Variational Quantum Algorithms for Trace Distance and Fidelity Estimation." arXiv preprint arXiv:2012.05768 (2020). [[pdf]](https://arxiv.org/pdf/2012.05768.pdf)
[5] Wang, Kun, et al. "Detecting and quantifying entanglement on near-term quantum devices." arXiv preprint arXiv:2012.14311 (2020). [[pdf]](https://arxiv.org/pdf/2012.14311.pdf)
[6] Zhao, Xuanqiang, et al. "LOCCNet: a machine learning framework for distributed quantum information processing." arXiv preprint arXiv:2101.12190 (2021). [[pdf]](https://arxiv.org/pdf/2101.12190.pdf)
"> Note that this tutorial is time-sensitive. And different computers will have individual differences. This tutorial does not guarantee that all computers can install it successfully.\n",
"\n",
"In deep learning, people usually use GPU for neural network model training because GPU has significant advantages in floating-point operations compared with CPU. Therefore, using GPU to train neural network models has gradually become a common choice. In Paddle Quantum, our quantum states and quantum gates are also represented by complex numbers based on floating-point numbers. If our model can be deployed on GPU for training, it will also significantly increase the training speed.\n"
"In deep learning, people usually use GPU for neural network model training because GPU has significant advantages in floating-point operations compared with CPU. Therefore, using GPU to train neural network models has gradually become a common choice. In Paddle Quantum, our quantum states and quantum gates are also represented by complex numbers based on floating-point numbers. If our model can be deployed on GPU for training, it will also significantly increase the training speed."
]
},
{
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@@ -142,7 +142,7 @@
"source": [
"We can enter `nvidia-smi` in the command line to view the usage of the GPU, including which programs are running on which GPUs, and its memory usage.\n",
"\n",
"Here, we take [VQE](https://github.com/PaddlePaddle/Quantum/blob/master/tutorial/VQE) as an example to illustrate how we should use GPU. First, import the related packages and define some variables and functions."
"Here, we take [VQE](../tutorial/quantum_simulation/VQE_EN.ipynb) as an example to illustrate how we should use GPU. First, import the related packages and define some variables and functions."
"The row vector (bra) $\\langle0| = |0\\rangle^\\dagger$ is the conjugate transpose of the column vector (ket) $|0\\rangle$.\n",
"\n",
"**Note:** For more information, please refer to [Wikipedia](https://en.wikipedia.org/wiki/Qubit).\n",
"\n"
"**Note:** For more information, please refer to [Wikipedia](https://en.wikipedia.org/wiki/Qubit)."
]
},
{
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@@ -198,7 +197,7 @@
"\n",
"We can expand the idea of single-qubit gates to multi-qubit. There are two ways to realize this expansion. The first is to apply single-qubit gates on selected qubits, while the other qubits are not operated. The figure below gives a concrete example:\n",
"\n",
"![intro-fig-hadamard](./figures/intro-fig-hadamard.png \"**Figure 2.** Circuit representation and interpretation of two-qubit logic operations. [[Picture source]](https://en.wikipedia.org/wiki/Quantum_logic_gate)\")\n",
"![intro-fig-hadamard](./figures/intro-fig-hadamard.png \"**Figure 2.** Circuit representation and interpretation of two-qubit logic operations. [[Image source]](https://en.wikipedia.org/wiki/Quantum_logic_gate)\")\n",
"\n",
"The quantum gate acting on two-qubit system can be expressed as a $4\\times4$ unitary matrix\n",
"\n",
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@@ -286,9 +285,7 @@
"\\tag{16}\n",
"$$\n",
"\n",
"Therefore, it is not difficult to see that the $X$ gate can be expressed as $R_x(\\pi)$. The following code will generate the $X$ gate:\n",
"\n",
"\n"
"Therefore, it is not difficult to see that the $X$ gate can be expressed as $R_x(\\pi)$. The following code will generate the $X$ gate:"
"When our task does not involve imaginary numbers, it is more efficient to use the circuit template `real_entangled_layer(theta, DEPTH)` ($R_y$ instead of $U_3$).\n",
"\n"
"When our task does not involve imaginary numbers, it is more efficient to use the circuit template `real_entangled_layer(theta, DEPTH)` ($R_y$ instead of $U_3$)."
]
},
{
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@@ -795,8 +822,7 @@
"\\tag{23}\n",
"$$\n",
"\n",
"Function `cir.run_density_matrix()` will be used in the following code. Here is an example:\n",
"\n"
"Function `cir.run_density_matrix()` will be used in the following code. Here is an example:"
